A reachability-based strategy for the time-optimal control of autonomous pursuers

Abstract

A control strategy for an autonomous pursuer is proposed in the reachability-based framework by using forward reachable sets (FRSs) to capture an evader vehicle time optimally. The FRS, which is a geometric representation of a vehicle's dynamic capability, allows the pursuers to determine if a single pursuer can capture the evader time optimally as well as to coordinate and maximize the chance of capturing the evader through the FRS coverage of multiple pursuers. The proposed strategy is then tested against the generic point-tracking (PT) algorithm in three different examples: (1) a single pursuer with sufficient manoeuvrability to capture an evader, (2) a single pursuer with significantly lower lateral manoeuvrability than the evader, and (3) multiple pursuers of the same manoeuvrability as in the second example. The numerical results demonstrate the superior performance of the proposed strategy over the PT algorithm when the pursuer has lower lateral manoeuvrability.

Pursuit-evasion games in dynamic flow fields via reachability set analysis

“…Backward Trajectory Tracking: Whenū ≥v, and after the reachability fronts of the pursuer and the evader meet at X f , the optimal controls of the pursuer and the evader can be achieved through (16). Also, we can compute the optimal trajectories X P and X E of the pursuer and the evader, respectively, by solving (14) and (15) backwards starting from X f at time t = T . Otherwise (v >ū), we simply replace ∇φ E with ∇φ E and follow the same procedure to find the optimal trajectories.…”

“…According to this approach, the reachable state space of both players is utilized to find the optimal controls of the pursuer and/or the evader. Reachable set analysis has been applied for performing missile/sensor trade-offs in homing guidance [12], for obtaining escape strategy under pursuit [13], and for finding pursuer control under control constraints [14].…”

A Supervisory Neural Network and Fuzzy Inference System Approach to an Unmanned Aerial Vehicle Tail-Chase Scenario

“…For example, leader-follower swarming behavior can utilize this to allow flight formations. There are several solutions for Tail-Chase vehicle control in low number scenarios, such as one-on-one, two-on-one, or even one-on-multiple [1][2][3]. The main difficulty when solving these types of problems is finding the boundaries in the state-space where switching actions lie.…”

Multiple-Pursuer/One-Evader Pursuit–Evasion Game in Dynamic Flowfields

“…According to this approach, the reachable state space of the pursuers and the evaders is used to find the optimal controls of the pursuer and/or the evader. A reachability set analysis has been applied in performing missile/sensor tradeoffs in homing guidance [16], in obtaining escape strategy under pursuit [17], and in finding pursuer control under control constraints [18].…”